# szenergy/awesome-lidar

😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.

Repository: https://github.com/szenergy/awesome-lidar
Canonical: https://ross.abutalabs.com/products/awesome-lidar
Homepage: https://szenergy.github.io/awesome-lidar/
License: CC0-1.0
License Family: permissive
Topics: awesome-list, awesome, lidar, pointcloud, point-cloud, 3d-lidar, 3d, autonomous-driving, obstacle-detection, slam, simulator, szenergy, sze, szegyetem
Last push: 2026-03-16T09:19:48+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 72, release rhythm 35, longevity 100
- inputs: {"age_days": 2333, "days_push": 170, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1331, forks 132 (observed 2026-08-28T04:04:24.333260+00:00)

## What it is
A curated awesome-list of LIDAR resources, including sensor manufacturers, datasets, point cloud processing libraries, frameworks, algorithms (SLAM, segmentation, object detection), and simulators. It serves as a reference index linking to external projects rather than being software itself.

## Use cases
- find lidar datasets for autonomous driving research
- discover point cloud processing libraries
- compare lidar sensor manufacturers
- find slam algorithms for lidar odometry
- locate lidar simulators for robotics
- learn about lidar-based object detection and tracking

## When to choose
- you need a starting point to discover lidar-related tools, datasets, or hardware
- you are researching autonomous driving or robotics perception
- you want a regularly maintained index of the lidar ecosystem

## When to avoid
- you need actual working software rather than links to other projects
- you need in-depth tutorials or documentation on a specific algorithm
- you need a non-lidar sensing modality like radar or camera-only resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, documentation, simulation, computer-vision, machine-learning
- domain: autonomous-vehicles, robotics, awesome-lists, computer-vision, simulation
- platform: cross-platform
- tags: lidar, point-cloud, slam, awesome-list, curated-list, sensors, datasets, obstacle-detection

## Member repositories
- szenergy/awesome-lidar (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:24.333260+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:45:21.484972+00:00, confidence not recorded.
  - readme: https://github.com/szenergy/awesome-lidar (fetched 2026-08-28T04:04:24.333260+00:00, sha a0fa670baa43)
  - homepage: https://szenergy.github.io/awesome-lidar/ (fetched 2026-08-29T12:04:48.265283+00:00, sha 611ba0c5e3b6)
- Data as of 2026-08-30T08:39:29.467469+00:00.